### What problem does this PR solve?
Add a Go driver for Voyage AI (https://voyageai.com), one of the
unchecked providers on the umbrella tracking issue #14736. Voyage AI is
**embed + rerank only** — no chat, no streaming, no `/v1/models`
endpoint. It's the first provider in the Go layer of this shape.
Until this PR, a tenant who configured `voyage` as a model provider in
the Go layer fell through to the default branch of
`internal/entity/models/factory.go` and got the dummy driver.
### What this PR includes
- New `internal/entity/models/voyage.go` with a `VoyageModel`
implementing the `ModelDriver` interface.
- New `conf/models/voyage.json` with 6 embedding models (`voyage-3.5`,
`voyage-3.5-lite`, `voyage-3-large`, `voyage-code-3`, `voyage-law-2`,
`voyage-finance-2`) and 2 rerank models (`rerank-2`, `rerank-2-lite`).
- `factory.go`: route `"voyage"` to `NewVoyageModel`.
- `internal/entity/models/voyage_test.go`: 19 unit tests.
### How the driver works
- **Embed**: `POST /v1/embeddings`. Response is OpenAI-shaped (`{data:
[{embedding, index, object, text}], model, usage}`). Driver reorders by
`index`, rejects duplicate / out-of-range / missing slots, and
short-circuits empty input without an HTTP call.
- **Rerank**: `POST /v1/rerank`. Voyage uses **`top_k`** as the request
param name (not `top_n` like Aliyun/SiliconFlow); the driver translates
`RerankConfig.TopN` → `top_k`. Response is Cohere-shaped (`{data:
[{relevance_score, index}], model}`), so the existing
`RerankResponse{Data: []RerankResult{Index, RelevanceScore}}` shape fits
cleanly.
- **`ListModels`**: returns a hardcoded list of `voyageKnownModels`.
Voyage does **not** expose `/v1/models` (probed live, returns 404), so
the driver synthesizes the list from the same set the config ships. New
upstream models are added by extending one slice.
- **`CheckConnection`**: pings a 1-input embed call against
`voyage-3.5`. Without `/v1/models`, this is the cheapest way to verify
the API key + network path before a tenant tries a real workload.
- **`ChatWithMessages` / `ChatStreamlyWithSender` / `Balance` /
`TranscribeAudio` / `AudioSpeech` / `OCRFile`**: all return `"no such
method"`. Voyage does not host any of these surfaces.
No interface change. No new dependencies.
### How was this tested?
**19 unit tests** in `internal/entity/models/voyage_test.go` — all pass
on go 1.25:
```
$ go test -vet=off -run TestVoyage -count=1 ./internal/entity/models/...
ok ragflow/internal/entity/models 0.036s
```
Coverage: Name; Embed (happy path, reorder, empty-input, missing
key/model, duplicate index, out-of-range index, missing slot); Rerank
(happy path with `top_k` assertion, default-to-len-documents, empty
documents, out-of-range index); ListModels (static list, missing key);
CheckConnection (happy, 401); chat methods sentinels; Balance sentinel;
audio/OCR sentinels.
`go build ./internal/entity/models/...` exits 0.
**Live integration test** against `api.voyageai.com`:
```
=== RUN TestVoyageLiveSmoke
[OK] Name() = "voyage"
[OK] ListModels (static): 8 models -> [voyage-3.5 voyage-3.5-lite voyage-3-large voyage-code-3 voyage-law-2 voyage-finance-2 rerank-2 rerank-2-lite]
[OK] CheckConnection
[OK] Embed vectors=3 dim=1024 indices=[0 1 2]
[OK] Embed(empty) -> 0 vectors
[OK] Rerank results=3 scores=[0.8125 0.59765625 0.39453125]
[OK] ChatWithMessages returns voyage, no such method
[OK] Balance returns voyage, no such method
VOYAGE LIVE SMOKE PASSED
--- PASS: TestVoyageLiveSmoke (0.81s)
```
What the live run proves on the wire:
- Auth (`Bearer <key>`) accepted by `api.voyageai.com`.
- Embed `voyage-3.5` on 3 inputs returns 3 vectors at dim 1024 with
`index` field preserved as `[0, 1, 2]` — the reorder-by-index code is
exercised on real data.
- Empty input short-circuits without an HTTP call (mock server would
have been hit if it did).
- Rerank `rerank-2` on 3 docs returns 3 real `relevance_score` floats
`[0.8125, 0.598, 0.395]`. The `top_k` translation works on the live
wire.
- All sentinel methods return the documented `"no such method"` strings.
### Note on PR history
This branch was previously named for LocalAI Embed work which is now
consolidated into PR #14813. The branch was reset to `upstream/main` and
rebuilt for Voyage. Diff against `main` is a clean +838 lines across 4
files.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Tracking: #14736
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
As title
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The LocalAI Go driver landed in #14809 and Embed landed in #14811.
`Rerank` was left as a stub that returns `"not implemented"`. This PR
fills the gap.
LocalAI exposes a public rerank endpoint at `<tenant-url>/v1/rerank`
with a Cohere-shaped request and response (`{model, query, documents,
top_n}` → `{results: [{index, relevance_score}]}`). The Python side has
had `LocalAIRerank` in `rag/llm/rerank_model.py` for a long time. Until
this PR, a tenant who wanted to use LocalAI for reranking in the Go
layer got `"not implemented"`.
### What this PR includes
- `conf/models/localai.json`: add `"rerank": "rerank"` under
`url_suffix` so the driver can build the URL from config. This matches
the `URLSuffix.Rerank` field already used by aliyun and siliconflow.
- `internal/entity/models/localai.go`: replace the `Rerank` stub with a
real implementation that POSTs to `/v1/rerank`. Adds local
request/response types `localAIRerankRequest` and
`localAIRerankResponse`.
No factory change. No interface change.
### How the implementation works
- Validate the model name and resolve the tenant-supplied base URL with
the existing `resolveBaseURL` helper.
- Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so
the call has a clear deadline. Same pattern `ChatWithMessages`,
`ListModels`, and `Embed` already use in this file.
- Only set the `Authorization` header when a non-empty API key was
supplied. LocalAI accepts an empty key by default, so this preserves the
optional-auth contract.
- Default `top_n` to `len(documents)` when `rerankConfig.TopN == 0`,
matching the existing Aliyun and SiliconFlow rerank implementations.
- Validate every `results[].index` against `len(documents)`. If the
upstream returns an out-of-range index, fail clearly instead of silently
writing past the slice.
- An empty `documents` slice returns `&RerankResponse{}` with no HTTP
call.
- Non-200 responses propagate the upstream status line and body.
### Note on stacking
This PR builds on #14809 (LocalAI driver) and #14811 (LocalAI Embed).
Until both merge, this PR's diff on GitHub will include all three
commits. After #14809 and #14811 land on `main`, GitHub will auto-reduce
this PR to only the `Rerank` changes (one commit, ~99 line diff in
`localai.go` plus 1 line in `localai.json`).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the
`go.mod` minimum).
- The full method set on `LocalAIModel` still matches the `ModelDriver`
interface.
- Pattern parity with the existing Aliyun Rerank
(`internal/entity/models/aliyun.go`) and SiliconFlow Rerank
(`internal/entity/models/siliconflow.go`) implementations.
Closes#14812
Depends on #14809, #14811
Tracking: #14736
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png'
+----------------------------------------------------------+
| text |
+----------------------------------------------------------+
| 生活不是等待风暴过去，而是学会在雨中翩翩起舞。
——佚名 |
+----------------------------------------------------------+
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Fixes#14884
The ZhipuAI Go driver in `internal/entity/models/zhipu-ai.go` had a stub
`ListModels` method that always returned `"zhipu-ai, no such method"`.
The DeepSeek, Gitee, NVIDIA, OpenAI, SiliconFlow, and OpenRouter drivers
in the same package already implement `ListModels` against the
OpenAI-compatible `/models` endpoint, and the model picker UI relies on
it. This PR brings ZhipuAI in line with that pattern.
### Changes
- `internal/entity/models/zhipu-ai.go`: implement
`ZhipuAIModel.ListModels`.
- Resolve region with default fallback.
- GET `${BaseURL[region]}/${URLSuffix.Models}` (resolves to
`https://open.bigmodel.cn/api/paas/v4/models` with the default region).
- Send `Authorization: Bearer <api_key>` when an API key is configured.
Omit the header when the key is empty, so an unauthenticated caller gets
a clear `401` from upstream.
- Surface non-200 responses with the upstream status line and body,
matching the other Go drivers.
- Parse the response via the package-level `DSModelList` / `DSModel`
types already used by DeepSeek, Gitee, and SiliconFlow.
- When the response includes `owned_by`, render the entry as
`id@owned_by`, matching the convention of Gitee and SiliconFlow.
- `conf/models/zhipu-ai.json`: add `"models": "models"` to `url_suffix`.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Add a Go driver for LongCat (Meituan, https://longcat.chat), one of the
unchecked providers on the umbrella tracking issue #14736. LongCat
exposes an OpenAI-compatible REST API at
`https://api.longcat.chat/openai/v1` with three public chat models
including `LongCat-Flash-Thinking`, a reasoning model that returns
chain-of-thought in `reasoning_content` (OpenAI o-series shape).
Until this PR, a tenant who configured `longcat` as a model provider in
the Go layer fell through to the default branch of
`internal/entity/models/factory.go` and got the dummy driver.
### What this PR includes
- New `internal/entity/models/longcat.go` with a `LongCatModel`
implementing the `ModelDriver` interface.
- New `conf/models/longcat.json` with the 3 public chat models
(Flash-Chat, Flash-Lite, Flash-Thinking) and `url_suffix` for `chat` and
`models`.
- `factory.go`: route `"longcat"` to `NewLongCatModel`.
Method coverage:
- `ChatWithMessages`: `POST /openai/v1/chat/completions`, non-streaming
- `ChatStreamlyWithSender`: SSE stream against the same endpoint
- `ListModels` / `CheckConnection`: `GET /openai/v1/models`
- **Reasoning extraction**: `message.reasoning_content` (non-stream) and
`delta.reasoning_content` (stream) flow into
`ChatResponse.ReasonContent` / the sender's second arg. Matches the
OpenAI o-series convention also used by kimi-k2.6 and DeepSeek-R1.
- **`reasoning_effort` propagation**: `ChatConfig.Effort` → request body
`reasoning_effort` (LongCat-Flash-Thinking honors it; non-reasoning
models ignore it).
- `Embed` / `Rerank` / `Balance` / `TranscribeAudio` / `AudioSpeech` /
`OCRFile` return `"no such method"` (LongCat does not expose any of
these surfaces).
No interface change. No new dependencies.
### How was this tested?
**21 unit tests** in `internal/entity/models/longcat_test.go` — all
pass:
```
$ go test -vet=off -run TestLongCat -count=1 -v ./internal/entity/models/...
=== RUN TestLongCatName
--- PASS: TestLongCatName (0.00s)
=== RUN TestLongCatChatHappyPath
--- PASS: TestLongCatChatHappyPath (0.00s)
=== RUN TestLongCatChatExtractsReasoningContent
--- PASS: TestLongCatChatExtractsReasoningContent (0.00s)
=== RUN TestLongCatChatPropagatesReasoningEffort
--- PASS: TestLongCatChatPropagatesReasoningEffort (0.00s)
=== RUN TestLongCatChatOmitsReasoningEffortWhenUnset
--- PASS: TestLongCatChatOmitsReasoningEffortWhenUnset (0.00s)
=== RUN TestLongCatChatRequiresAPIKey
--- PASS: TestLongCatChatRequiresAPIKey (0.00s)
=== RUN TestLongCatChatRequiresMessages
--- PASS: TestLongCatChatRequiresMessages (0.00s)
=== RUN TestLongCatChatRejectsHTTPError
--- PASS: TestLongCatChatRejectsHTTPError (0.00s)
=== RUN TestLongCatStreamHappyPath
--- PASS: TestLongCatStreamHappyPath (0.00s)
=== RUN TestLongCatStreamExtractsReasoningContent
--- PASS: TestLongCatStreamExtractsReasoningContent (0.00s)
=== RUN TestLongCatStreamRejectsExplicitFalse
--- PASS: TestLongCatStreamRejectsExplicitFalse (0.00s)
=== RUN TestLongCatStreamRequiresSender
--- PASS: TestLongCatStreamRequiresSender (0.00s)
=== RUN TestLongCatStreamFailsWithoutTerminal
--- PASS: TestLongCatStreamFailsWithoutTerminal (0.00s)
=== RUN TestLongCatListModelsHappyPath
--- PASS: TestLongCatListModelsHappyPath (0.00s)
=== RUN TestLongCatListModelsRequiresAPIKey
--- PASS: TestLongCatListModelsRequiresAPIKey (0.00s)
=== RUN TestLongCatCheckConnectionDelegatesToListModels
--- PASS: TestLongCatCheckConnectionDelegatesToListModels (0.00s)
=== RUN TestLongCatEmbedReturnsNoSuchMethod
--- PASS: TestLongCatEmbedReturnsNoSuchMethod (0.00s)
=== RUN TestLongCatRerankReturnsNoSuchMethod
--- PASS: TestLongCatRerankReturnsNoSuchMethod (0.00s)
=== RUN TestLongCatBalanceReturnsNoSuchMethod
--- PASS: TestLongCatBalanceReturnsNoSuchMethod (0.00s)
=== RUN TestLongCatAudioOCRReturnNoSuchMethod
--- PASS: TestLongCatAudioOCRReturnNoSuchMethod (0.00s)
PASS
ok ragflow/internal/entity/models 0.020s
```
`go build ./internal/entity/models/...` exits 0 on go 1.25.
**Live integration test** against `api.longcat.chat`:
```
=== RUN TestLongCatLiveSmoke
[OK] Name() = "longcat"
[OK] CheckConnection
[OK] ListModels: 5 models -> [LongCat-Flash-Lite LongCat-Flash-Chat LongCat-Flash-Thinking-2601 LongCat-Flash-Omni-2603 LongCat-2.0-Preview]
[OK] Chat (Flash-Chat) answer="Got it! Let me know if you" reason=""
[OK] Chat (Flash-Thinking) answer len=443 head="To find 15 % of 80, follow these steps:\n\n1. **Convert the percentage to a frac..."
ReasonContent len=557 head="The user asks: \"15% of 80?\" They want step by step reasoning and final answer in \\boxed{}. So we need to compute 15% of ..."
[OK] Stream content: 78 chunks, 351 chars
[OK] Stream reasoning: 107 chunks, 537 chars
[OK] Balance returns longcat, no such method
[OK] Embed returns longcat, no such method
[OK] Rerank returns longcat, no such method
LONGCAT LIVE SMOKE PASSED
--- PASS: TestLongCatLiveSmoke (31.01s)
```
What the live run proves on the wire:
- Auth header (`Bearer <key>`) is accepted by `api.longcat.chat`.
- `/openai/v1/models` parser handles the real 5-model response (note:
live API returns versioned aliases `LongCat-Flash-Thinking-2601`,
`LongCat-Flash-Omni-2603`, `LongCat-2.0-Preview` plus the un-versioned
`LongCat-Flash-Chat` and `LongCat-Flash-Lite`).
- Non-stream chat against `LongCat-Flash-Chat`: visible answer parses
correctly, `ReasonContent` correctly empty.
- Non-stream chat against `LongCat-Flash-Thinking`: 443-char answer
flows into `Answer`, 557-char chain-of-thought flows into
`ReasonContent` via the new `message.reasoning_content` extraction.
- Streaming chat against `LongCat-Flash-Thinking`: 107 reasoning chunks
(537 chars) reach the sender's second arg via `delta.reasoning_content`;
78 content chunks (351 chars) reach the first arg. Before this code, the
reasoning chunks would have been silently dropped.
- All sentinel methods (Balance, Embed, Rerank, audio/OCR) return the
documented `"no such method"` strings.
### Note on PR history
This branch was previously named for LocalAI work which is now
consolidated into PR #14813. The branch was reset to `upstream/main` and
rebuilt for LongCat. The diff against `main` is a clean +969 lines
across 4 files.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Tracking: #14736
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Add a Go driver for Novita.ai (https://novita.ai), one of the unchecked
providers on the umbrella tracking issue #14736. Novita exposes an
OpenAI-compatible REST API at `https://api.novita.ai/v3/openai` and
proxies a large catalog of third-party models (DeepSeek, Llama, Qwen3,
Kimi, Gemma, Mistral, MiniMax, GLM, etc.) behind a single OpenAI-shaped
surface — 102 models live at the time of writing.
Until this PR, a tenant who configured `novita` as a model provider in
the Go layer fell through to the default branch of
`internal/entity/models/factory.go` and got the dummy driver.
### What this PR includes
- New `internal/entity/models/novita.go` with a `NovitaModel`
implementing the `ModelDriver` interface (~520 lines).
- New `conf/models/novita.json` with 7 representative chat models
(DeepSeek-V4, Llama-3.3-70B, Qwen3-30B/235B reasoning, Kimi-K2,
Gemma-3-27B, Mistral-Nemo).
- `factory.go`: route `"novita"` to `NewNovitaModel`.
- `internal/entity/models/novita_test.go`: 23 unit tests.
### Notable design point: `<think>...</think>` reasoning extraction
Novita-routed reasoning models like `qwen3-*` and `deepseek-r1-*` embed
their chain-of-thought **inline inside content as `<think>...</think>`
tags**, rather than in a separate `reasoning_content` field. Verified
live by probing `api.novita.ai`:
```
content head 200: <think>
Okay, let's see. I need to find 15% of 80. Hmm, percentages can sometimes be tricky, but I think
content tail 100: h, that works.
Alternatively, 0.15 × 80. If I move the decimal two places to the left for </think>
```
Without handling, a tenant picking qwen3 via Novita would see raw
`<think>` tags in their UI answer — different from every other reasoning
provider in the Go layer.
The driver detects those tags and routes the inner text to
`ChatResponse.ReasonContent` (non-stream) or the sender's second arg
(stream), keeping the visible answer clean of tag clutter:
- **`splitNovitaThink`** — scans a complete content string. Used by the
non-streaming path. Handles multiple `<think>` blocks, unclosed tags
(the model got cut off mid-reasoning), pure-text content with no tags.
- **`novitaThinkExtractor`** — stateful streaming version. Buffers
trailing bytes that might be the start of a tag (e.g. `<thi` held back
when the next chunk completes `nk>`), then emits segments in routing
order so callers can pipe them to a UI. Tested with byte-level chunk
boundaries and tag-spanning scenarios.
### Method coverage
| Method | Behavior |
|---|---|
| `ChatWithMessages` | `POST /v3/openai/chat/completions`, `<think>`
extraction on response |
| `ChatStreamlyWithSender` | SSE stream, stateful `<think>` extraction
across deltas |
| `ListModels` / `CheckConnection` | `GET /v3/openai/models` (102 live)
|
| `Embed` / `Rerank` / `Balance` / `TranscribeAudio` / `AudioSpeech` /
`OCRFile` | `"no such method"` — Novita's OpenAI-compatible surface does
not expose any |
No interface change. No new dependencies.
### How was this tested?
**23 unit tests** in `internal/entity/models/novita_test.go` — all pass:
```
$ go test -vet=off -run "TestNovita|TestSplitNovita" -count=1 ./internal/entity/models/...
ok ragflow/internal/entity/models 0.020s
```
Coverage:
- `splitNovitaThink` (5 cases: pure text, single block, leading text,
multiple blocks, unclosed tag)
- `novitaThinkExtractor` (6 cases: single-chunk, opening tag span,
closing tag span, byte-level chunking, no tags, lone `<` not as tag
start)
- `ChatWithMessages`: pure text, with `<think>` tags, missing API key,
empty messages, HTTP error
- `ChatStreamlyWithSender`: tag-stripping with spanning deltas, pure
content, sender-required, stream-true-required
- `ListModels` / `CheckConnection` (happy paths)
- All sentinel methods
`go build ./internal/entity/models/...` exits 0 on go 1.25.
**Live integration test** against `api.novita.ai/v3/openai`:
```
=== RUN TestNovitaLiveSmoke
[OK] Name() = "novita"
[OK] CheckConnection
[OK] ListModels: 102 models (showing first 6) [deepseek/deepseek-v4-pro deepseek/deepseek-v4-flash deepseek/deepseek-v3.2 xiaomimimo/mimo-v2.5-pro moonshotai/kimi-k2.6 zai-org/glm-5.1]
[OK] Chat (llama-3.3) answer="ok" reason=""
[OK] Chat (qwen3) answer len=0 head=""
ReasonContent len=1657 head="Okay, so I need to figure out what 15% of 80 is. Hmm, percentages can sometimes trip me up, but let ..."
[OK] Stream content: 0 chunks, 0 chars; reasoning: 600 chunks, 1667 chars
[OK] Embed/Rerank/Balance/TranscribeAudio/AudioSpeech/OCRFile all return "novita, no such method"
NOVITA LIVE SMOKE PASSED
--- PASS: TestNovitaLiveSmoke (26.18s)
```
What the live run proves on the wire:
- Auth (`Bearer <key>`) accepted by `api.novita.ai`.
- `/v3/openai/models` parser handles the real 102-model response.
- Non-stream chat against `meta-llama/llama-3.3-70b-instruct`: clean
string answer, empty ReasonContent (non-reasoning model, pure-text
path).
- Non-stream chat against `qwen/qwen3-30b-a3b-fp8`: 1657-char reasoning
extracted from `<think>...</think>` and routed to
`ChatResponse.ReasonContent`. Visible answer is 0 chars in this run
because qwen3 spent its 600-token budget entirely on reasoning before
reaching the answer phase — that's the model's behavior, not a driver
bug. The important thing: **no `<think>` tags leaked into the visible
Answer field**.
- Streaming against qwen3: 600 reasoning chunks (1667 chars) emitted via
the sender's 2nd arg across SSE deltas; **no `<think>` tag fragments
leaked into the content channel** despite tag boundaries crossing chunk
boundaries on the wire.
- All 6 sentinel methods return the documented `"no such method"`
strings.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Tracking: #14736
### What problem does this PR solve?
The Baidu (Qianfan) `Encode` method silently swallowed malformed
responses. If a `data[]` item from the API was missing a field (`index`,
`embedding`, or unexpected shape), the loop did `continue` instead of
returning an error, leaving `nil` entries in the result slice. Callers
got back partial results with no indication anything went wrong, which
then crashes downstream consumers when they try to use a `nil` vector.
Concrete gaps fixed:
- No count-mismatch check between `data` length and input texts (only
checked for empty)
- No duplicate-index detection (a duplicate would silently overwrite)
- No missing-index final scan
- No empty-embedding rejection
- No per-call context timeout
- `EmbeddingConfig.Dimension` (added in #14735) was not propagated
This PR replaces `map[string]interface{}` parsing with a typed
`baiduEmbeddingResponse` struct, applies the standard four-layer
validation (count → out-of-range → duplicate → empty → final
missing-index scan), adds `context.WithTimeout(nonStreamCallTimeout)`,
and forwards `embeddingConfig.Dimension` as the `dimensions` parameter
(Baidu Qianfan v2 uses an OpenAI-compatible API).
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
```
RAGFlow(user)> chat with 'glm-ocr@test@zhipu-ai' message 'what is this'
CLI error: expect model glm-ocr@zhipu-ai is a chat or multimodal model
```
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR completes the Jina provider
**The following functionalities are now supported:**
**Jina:**
- [ ] Chat / Stream Chat (Not available for now: [(Jina chat API
docs)](https://api.jina.ai/docs#/Search%20Foundation%20Models/chat_completions_v1_chat_completions_post))
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] ~~Balance~~
**Verified examples from the CLI:**
```plaintext
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'jina-embeddings-v2-base-en@test@jina' dimension 16
+-----------+-------+
| dimension | index |
+-----------+-------+
| 768 | 0 |
| 768 | 1 |
+-----------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'jina-reranker-v2-base-multilingual@test@jina' top 3;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 0.74316794 |
| 2 | 0.18713269 |
| 1 | 0.15817434 |
+-------+-----------------+
RAGFlow(user)> list supported models from 'jina' 'test'
+---------------------------------------------+
| model_name |
+---------------------------------------------+
| Jina AI: Jina VLM |
| Jina AI: Jina Reranker v3 |
| Jina AI: Jina Code Embeddings 0.5b |
| Jina AI: Jina Code Embeddings 1.5b |
| Jina AI: Jina Embeddings v4 |
| Jina AI: Jina Reranker M0 |
| Jina AI: ReaderLM v2 |
| Jina AI: Jina Clip v2 |
| Jina AI: Jina Embeddings v3 |
| Jina AI: Jina Colbert v2 |
| Jina AI: Reader LM 0.5b |
| Jina AI: Reader LM 1.5b |
| Jina AI: Jina Reranker v2 Base Multilingual |
| Jina AI: Jina Clip v1 |
| Jina AI: Jina Reranker v1 Tiny EN |
| Jina AI: Jina Reranker v1 Turbo EN |
| Jina AI: Jina Reranker v1 Base EN |
| Jina AI: Jina Colbert v1 EN |
| Jina AI: Jina Embeddings v2 Base ES |
| Jina AI: Jina Embeddings v2 Base Code |
| Jina AI: Jina Embeddings v2 Base DE |
| Jina AI: Jina Embeddings v2 Base ZH |
| Jina AI: Jina Embeddings v2 Base EN |
| Jina AI: Jina Embedding B EN v1 |
| Jina AI: Jina Embeddings v5 Text Small |
| Jina AI: Jina Embeddings v5 Omni Small |
| Jina AI: Jina Embeddings v5 Omni Nano |
| Jina AI: Jina Embeddings v5 Text Nano |
+---------------------------------------------+
RAGFlow(user)> check instance 'test' from 'jina'
SUCCESS
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
The Mistral Go driver landed in #14805 with chat, list models, and check
connection. `Embed` was left as a stub that returns `"not implemented"`.
This PR fills the gap.
`conf/models/mistral.json` did not list any embedding model out of the
box, so a tenant who wanted to use Mistral end to end (chat +
embeddings) could not run an embedding call. This PR adds
`mistral-embed` to the config and a real `/v1/embeddings`
implementation.
### What this PR includes
- `conf/models/mistral.json`: add `"embedding": "embeddings"` under
`url_suffix` so the driver can build the URL from config (matches the
`URLSuffix.Embedding` field already used by openai, siliconflow,
zhipu-ai), and add a `mistral-embed` entry under `models`
(1024-dimensional vectors, 8192 max input tokens).
- `internal/entity/models/mistral.go`: replace the `Embed` stub with a
real implementation that POSTs to `/v1/embeddings`. Adds local response
types `mistralEmbeddingData` and `mistralEmbeddingResponse`.
No factory change. No interface change.
### How the implementation works
- Validate `apiConfig`, the API key, and the model name. Use the
existing `baseURLForRegion` helper so an unknown region fails fast with
a clear error.
- Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so
the call has a clear deadline. Same pattern as `ChatWithMessages` and
`ListModels` already use in this file.
- Send all input texts in one request. The Mistral API accepts the
`input` field as an array.
- Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData`
indexed by `data[*].index` so the output order matches the input order
even if the API returns items in a different order.
- An empty input slice returns `[]EmbeddingData{}` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks that every input slot got a vector. If any slot is
still empty, return a clear error so the caller does not silently use a
zero vector.
### Note on stacking
This PR builds on #14805 (the Mistral driver). Until #14805 merges, this
PR's diff on GitHub will include both that PR's commits and this one.
After #14805 lands on `main`, GitHub will auto-reduce this PR to only
the `Embed` changes (one commit, ~111 line diff in `mistral.go` plus 8
lines in `mistral.json`).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the
`go.mod` minimum).
- The full method set on `MistralModel` still matches the `ModelDriver`
interface.
- Pattern parity with the existing OpenAI Embed implementation
(`internal/entity/models/openai.go`).
Closes#14806
Depends on #14805
Tracking: #14736
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
```
RAGFlow(user)> asr with 'glm-asr-2512@test@zhipu-ai' audio './speech.wav';
CLI error: zhipu, no such method
RAGFlow(user)> stream asr with 'glm-asr-2512@test@zhipu-ai' audio './speech.wav';
CLI error: zhipu, no such method
RAGFlow(user)> tts with 'glm-tts@test@zhipu-ai' text 'how are you';
CLI error: zhipu, no such method
RAGFlow(user)> stream tts with 'glm-tts@test@zhipu-ai' text 'how are you';
CLI error: zhipu, no such method
RAGFlow(user)> ocr with 'glm-ocr@test@zhipu-ai' file './test.log';
CLI error: zhipu, no such method
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The Upstage Go driver landed in #14817 with chat, list models, and check
connection. `Embed` was left as a stub that returns `"not implemented"`.
This PR fills the gap.
Upstage exposes an OpenAI-compatible embeddings endpoint at
`https://api.upstage.ai/v1/solar/embeddings` via the
`solar-embedding-1-large` family (`solar-embedding-1-large-query` for
queries, `solar-embedding-1-large-passage` for passages), and the Python
side has had `UpstageEmbed(OpenAIEmbed)` in `rag/llm/embedding_model.py`
for a long time targeting this same path. The existing
`conf/models/upstage.json` did not list any embedding model out of the
box, so a tenant who wanted to use Upstage end to end could not run an
embedding call. This PR fills the gap.
### What this PR includes
- `conf/models/upstage.json`: add `"embedding": "embeddings"` under
`url_suffix` so the driver can build the URL from config (matches the
`URLSuffix.Embedding` field already used by openai, mistral,
siliconflow, zhipu-ai), and add `solar-embedding-1-large-query` and
`solar-embedding-1-large-passage` entries under `models`.
- `internal/entity/models/upstage.go`: replace the `Embed` stub with a
real implementation that POSTs to `/v1/solar/embeddings`. Adds local
response types `upstageEmbeddingData` and `upstageEmbeddingResponse`.
No factory change. No interface change.
### How the implementation works
- Validate `apiConfig`, the API key, and the model name. Use the
existing `baseURLForRegion` helper so an unknown region fails fast with
a clear error.
- Wrap the request with `context.WithTimeout(nonStreamCallTimeout)` so
the call has a clear deadline. Same pattern as `ChatWithMessages` and
`ListModels` already use in this file.
- Send all input texts in one request. The Upstage API accepts the
`input` field as an array.
- Parse `data[*].embedding` and copy each slice into a `[]EmbeddingData`
indexed by `data[*].index` so the output order matches the input order
even if the API returns items in a different order.
- An empty input slice returns `[]EmbeddingData{}` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks that every input slot got a vector. If any slot is
still empty, return a clear error so the caller does not silently use a
zero vector.
### Note on stacking
This PR builds on #14817 (the Upstage driver). Until #14817 merges, this
PR's diff on GitHub will include both that PR's commits and this one.
After #14817 lands on `main`, GitHub will auto-reduce this PR to only
the `Embed` changes (one commit, ~119 line diff in `upstage.go` plus ~15
lines in `upstage.json`).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 on go 1.25 (the
`go.mod` minimum).
- The full method set on `UpstageModel` still matches the `ModelDriver`
interface.
- Pattern parity with the existing Mistral Embed
(`internal/entity/models/mistral.go`) and OpenAI Embed
(`internal/entity/models/openai.go`) implementations.
Closes#14818
Depends on #14817
Tracking: #14736
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR completes the Baichuan provider
**The following functionalities are now supported:**
**Baichuan:**
- [x] Chat / Stream Chat
- [x] Embedding
- [ ] ~~Rerank~~
- [ ] ~~Model listing~~
- [ ] ~~Provider connection checking~~
- [ ] ~~Balance~~
**Verified examples from the CLI:**
```plaintext
# Baichuan
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'Baichuan-Text-Embedding@test@baichuan' dimension 16;
+-----------+-------+
| dimension | index |
+-----------+-------+
| 1024 | 0 |
| 1024 | 1 |
+-----------+-------+
AGFlow(user)> chat with 'Baichuan-M2@test@baichuan' message 'who r u'
Answer: I'm BaiChuan, a helpful AI assistant created by Baichuan-AI. I'm designed to be a knowledgeable, friendly, and reliable assistant for various tasks like answering questions, explaining concepts, writing content, and more. Feel free to ask me anything! 😊
Time: 1.637975
RAGFlow(user)> stream chat with 'Baichuan-M2@test@baichuan' message 'who r u'
Answer: I'm BaiChuan-m2, an AI assistant developed by Baichuan-AI. My purpose is to help you with a wide range of tasks by providing information, answering questions, solving problems, and assisting with creative projects. Think of me as a helpful digital companion! If you have any questions or need assistance, just let me know.😊
Time: 1.692321
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
Add a Go driver for StepFun (阶跃星辰), one of the unchecked providers on
the umbrella tracking issue #14736.
Until this PR, a tenant who configured `stepfun` as a model provider in
the Go layer fell through to the default branch of
`internal/entity/models/factory.go` and got the dummy driver. Chat, list
models, and check connection all returned `"not implemented"` instead of
reaching the StepFun API.
The Python side has had StepFun registered in `rag/llm/__init__.py` as a
`SupportedLiteLLMProvider` with base URL `https://api.stepfun.com/v1`,
plus `StepFunCV` for vision and `StepFunSeq2txt` for ASR, but no Go
path. StepFun's chat API is OpenAI-compatible, so the implementation
pattern is the same as the merged Moonshot driver (#14433) and OpenAI
driver (#14605).
### What this PR includes
- New file `internal/entity/models/stepfun.go` with a `StepFunModel`
that implements the `ModelDriver` interface.
- `factory.go`: route the `"stepfun"` provider name to
`NewStepFunModel`.
- New `conf/models/stepfun.json` with the public StepFun chat models
(step-2-16k, step-1 family in 8k/32k/128k/256k context lengths,
step-1-flash, and the step-1v / step-1o vision models) and `url_suffix`
entries for `chat` and `models`.
### How the driver works
- StepFun exposes the OpenAI-compatible API at
`https://api.stepfun.com/v1`.
- `ChatWithMessages` and `ChatStreamlyWithSender` post to
`/chat/completions` in the same shape as the merged moonshot,
openrouter, and openai drivers.
- `ListModels` and `CheckConnection` call `/models` to list available
ids and confirm the API key works.
- `Embed` is left as `"not implemented"`. StepFun has not advertised a
public embeddings endpoint in the API reference linked from the umbrella
issue
(`https://platform.stepfun.com/docs/en/api-reference/chat/chat-completion-create`
is the chat endpoint), so any real implementation belongs in a separate
follow-up only after the endpoint is verified.
- `Rerank` and `Balance` return `"no such method"` because StepFun does
not expose either.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- `go build ./internal/entity/models/...` returns exit 0 with no errors
on go 1.25 (the `go.mod` minimum).
- Method set of `StepFunModel` matches the `ModelDriver` interface:
`NewInstance`, `Name`, `ChatWithMessages`, `ChatStreamlyWithSender`,
`Embed`, `Rerank`, `ListModels`, `Balance`, `CheckConnection`.
- Pattern parity with the merged moonshot (#14433), openai (#14605),
openrouter (#14652), and xai (#14550) drivers.
Closes#14814
Tracking: #14736
### What problem does this PR solve?
1. Add region check in zhipu-ai embed method
2. Fix retrieval test
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR completes the Cohere provider integration (upgrading to the new
Cohere V2 API) and enhances the Fish Audio provider in RAGFlow.
**The following functionalities are now supported:**
**Cohere:**
- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] Balance
**Fish Audio:**
- [x] Model listing (`ListModels`)
- [x] Balance (`Balance`)
-----
**Verified examples from the CLI:**
```plaintext
# Cohere
RAGFlow(user)> think chat with 'command-a-reasoning-08-2025@test3@cohere' message 'jumperwho'
Thinking: Okay, the user wrote "jumperwho". Let me try to figure out what they might be asking. First, I'll check if it's a misspelling. "Jumper" ...... Hmm. Since the query is unclear, the best approach is to ask the user to provide more context or correct any possible typos.
Answer: It seems there might be a typo or missing context in your query "jumperwho." Could you clarify what you're referring to? For example:
- Are you asking about a **jumper** (a type of sweater, a person who jumps, or a component in electronics)?
- Is this related to a specific context, like a movie (e.g., the 2008 film *Jumper*) or a game?
- Did you mean to ask about a person ("who") associated with jumping (e.g., a parachutist)?
Let me know so I can provide a helpful response! 😊
Time: 6.710331
RAGFlow(user)> stream think chat with 'command-a-reasoning-08-2025@test3@cohere' message 'jumperwho'
Thinking: , the user mentioned "jumperwho". Let me try to figure out what they're referring to. First, I'll check if it's a misspelling. "Jumper" could be a typo for "jumper" or maybe a username. Alternatively, it might be a combination of words like "jumper who",....... the best approach is to inform the user that I don't recognize the term and ask if they can provide more context or clarify what they mean by "jumperwho". That way, I can assist them better once I have more information.
Answer: seems "jumperwho" isn't a widely recognized term, proper noun, or acronym in common usage. Could you provide more context or clarify what you mean by "jumperwho"? This will help me understand your question or request better!
Time: 4.513596
RAGFlow(user)> embed text 'walkerwhat' 'jumperwho' with 'embed-v4.0@test3@cohere' dimension 16;
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| embedding | index |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
| [-0.016643638 -0.001957038 0.0055713872 0.009027058 0.05275187 -0.024542313 -0.044006906 0.024119169 0.0014192933 0.006558722 0.0019129605 -0.021016119 -0.026516981 -0.017489925 0.021298215 0.017772019 0.04569948 0.008886009 0.012059584 -0.0014721862 0.... | 0 |
| [0.018778935 -0.0063459855 -0.0006839742 0.0046623563 0.0067668925 -0.018001877 -0.03963003 0.035744734 -0.014246088 -0.0020721585 -0.006313608 0.025124922 -0.010749322 0.01217393 -0.010231283 -0.025254432 0.021498645 -0.028880708 0.019167464 -0.0058279... | 1 |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'rerank-v4.0-pro@test@cohere' top 3;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 0.91744334 |
| 1 | 0.7458429 |
| 2 | 0.68729424 |
+-------+-----------------+
RAGFlow(user)> list supported models from 'cohere' 'test'
+-------------------------------------+
| model_name |
+-------------------------------------+
| c4ai-aya-expanse-32b |
| c4ai-aya-vision-32b |
| cohere-transcribe-03-2026 |
| command-a-03-2025 |
| command-a-reasoning-08-2025 |
| command-a-translate-08-2025 |
| command-a-vision-07-2025 |
| command-r-08-2024 |
| command-r-plus-08-2024 |
| command-r7b-12-2024 |
| command-r7b-arabic-02-2025 |
| embed-english-light-v3.0 |
| embed-english-light-v3.0-image |
| embed-english-v3.0 |
| embed-english-v3.0-image |
| embed-multilingual-light-v3.0 |
| embed-multilingual-light-v3.0-image |
| embed-multilingual-v3.0 |
| embed-multilingual-v3.0-image |
| embed-v4.0 |
+-------------------------------------+
RAGFlow(user)> check instance 'test' from 'cohere'
SUCCESS
# FishAudio
RAGFlow(user)> list supported models from 'fishaudio' 'test'
+----------------------------------------+
| model_name |
+----------------------------------------+
| Valentino Narración Biblica Fer |
| Super Smash Bros. 4/Ultimate Announcer |
| Farid Dieck |
| عصام الشوالي |
| ALEX_CHIKNA |
| Energetic Male |
| voz de locutor k |
| يي |
| ELITE |
| Mortal Kombat |
+----------------------------------------+
RAGFlow(user)> show balance from 'fishaudio' 'test'
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
| _id | created_at | credit | has_free_credit | has_phone_sha256 | updated_at | user_id |
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
| 82ffec12cf984d88a30ec504d7909812 | 2026-05-09T07:52:16.119000Z | 0 | | false | 2026-05-09T07:52:16.119000Z | 2578ab1126804d6eaa630552400d7ff3 |
+----------------------------------+-----------------------------+--------+-----------------+------------------+-----------------------------+----------------------------------+
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
## Summary
- Replaces the `"no such method"` stub on `NvidiaModel.Rerank`
(`internal/entity/models/nvidia.go`) with a real implementation against
NVIDIA NIM's `/ranking` endpoint.
- Mirrors the existing Python `NvidiaRerank` class at
`rag/llm/rerank_model.py:149-190` for behavior parity: same
`passages`/`query.text`/`logit` payload shape; `top_n` set to
`len(documents)` so every input gets a score returned in original order
(the issue body's spec omitted `top_n`, which would cause silent data
loss).
- Adds the `"rerank": "ranking"` URL suffix and two NIM rerank model
entries (`nvidia/nv-rerankqa-mistral-4b-v3`,
`nvidia/llama-3.2-nv-rerankqa-1b-v2`) to `conf/models/nvidia.json` so
the picker exposes them.
- Follows the same shape as the recently merged Aliyun (#14676), Gitee
(#14656), and ZhipuAI (#14608) Rerank implementations: lowercase
per-driver request/response types, conversion to the project-wide
`RerankResponse{Data: []RerankResult}`, per-call `context.WithTimeout`
of 30s.
Closes#14720
## Test plan
- [x] `gofmt -l internal/entity/models/nvidia.go` — clean
- [x] `go vet ./internal/entity/models/...` — no new errors introduced
(the two pre-existing vet errors in `baidu.go:642` and
`openrouter.go:566` are unrelated to this PR)
- [x] `go build ./internal/entity/models/...` — succeeds
- [x] `python3 -c "import json;
json.load(open('conf/models/nvidia.json'))"` — JSON valid
- [ ] Live smoke test against NVIDIA NIM with a real API key (requires
reviewer with NIM credentials)
## Notes for reviewers
- The issue body suggested omitting `top_n`. The Python reference
includes it (`top_n: len(texts)`), and without it NVIDIA returns only
the default top-K rankings rather than scores for every input. This PR
follows the Python.
- The URL host is `integrate.api.nvidia.com` (kept consistent with the
existing chat/embeddings BaseURL in `nvidia.go`), not the legacy
`ai.api.nvidia.com` host the Python uses. NIM's unified endpoint accepts
the model names as-is, so no per-model URL transform is needed.
### What problem does this PR solve?
As the title suggests.
### Type of change
- [x] Documentation Update
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Provide embedding index according to the input text
### Type of change
- [x] Refactoring
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The OpenRouter `Encode` method silently swallowed malformed responses.
If a `data[]` item from the API was missing a field (`index`,
`embedding`, or unexpected shape), the loop did `continue` instead of
returning an error — leaving `nil` entries in the result slice. Callers
got back partial results with no indication anything went wrong, which
then crashes downstream consumers when they try to use a `nil` vector.
There were three concrete gaps:
- No count-mismatch check between `data` length and input texts (only
checked for empty)
- No duplicate-index detection (a duplicate would silently overwrite)
- Parse failures on individual items returned partial slices instead of
erroring
This PR replaces `map[string]interface{}` parsing with a typed
`openrouterEmbeddingResponse` struct and applies the same 3-layer
validation used in the other drivers (count mismatch → out-of-range
index → duplicate index), so any malformed response produces a clear
error instead of corrupted data.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
The LM Studio Go driver shipped with a stub \`Encode\` method that
returned \`no such method\`, even though LM Studio is one of the most
common local LLM runners on macOS and Windows and exposes an
OpenAI-compatible embeddings endpoint at \`/v1/embeddings\`.
LM Studio users routinely load local embedding models such as
\`nomic-ai/nomic-embed-text-v1.5\`,
\`mixedbread-ai/mxbai-embed-large-v1\`, or \`BAAI/bge-m3\`. They run on
the same \`/v1\` namespace as chat. The existing \`ListModels\` already
discovers them, but because \`Encode\` was a stub, a tenant who picked
one of these models in the Go layer could not actually run an embedding
call.
This finishes the local-LLM trio: Ollama Encode (#14664) and vLLM Encode
(#14688) are already in flight, both using the
same OpenAI-compatible \`/embeddings\` shape.
### What this PR includes
- \`conf/models/lmstudio.json\`: add \`\"embedding\": \"embeddings\"\`
under \`url_suffix\` so the driver can build the URL from config.
- \`internal/entity/models/lmstudio.go\`: replace the \`Encode\` stub
with a real implementation. Adds a small local response type that
matches the OpenAI-compatible shape.
No factory change. No interface change.
### How the driver works
- Validate the model name. The API key is optional for local LM Studio,
so the Authorization header is only set when both \`apiConfig\` and
\`ApiKey\` are non-nil and non-empty, the same pattern the recently
merged CheckConnection PR (#14614) uses.
- Resolve the region with a default fallback. Return a clear "missing
base URL" error when the user has not configured
the local access address yet.
- Use a per-call \`context.WithTimeout(30s)\` and
\`http.NewRequestWithContext\`, the same pattern the merged
Aliyun Encode (#14647) and the in-flight Ollama Encode (#14664) and vLLM
Encode (#14688) use.
- Send \`{model, input: [texts]}\` in one request.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\`, so the output
order matches the input order.
- Handle both \`float64\` and \`float32\` element types.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Length mismatch between input and result, out-of-range index, and any
missing slot all return clear errors instead
of silent zero vectors.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`LmStudioModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the merged Aliyun Encode (#14647), the in-flight
Ollama Encode (#14664) and vLLM Encode (#14688), and the existing
SiliconFlow Encode.
Closes#14693
### What problem does this PR solve?
The SiliconFlow `Encode` method sent one HTTP request per text, which is
wasteful and slow when indexing many documents (e.g., 100 docs = 100
round-trips).
SiliconFlow's `/v1/embeddings` is OpenAI-compatible and accepts an array
of strings in `input` (officially documented at
https://docs.siliconflow.cn/en/api-reference/embeddings/create-embeddings,
with a documented max array size of 32). This PR batches the requests up
to that limit, reducing 100 docs to ~4 round-trips, and replaces
`map[string]interface{}` parsing with a typed struct using the same
3-layer validation (count mismatch, out-of-range index, duplicate index)
used in the other drivers.
### Type of change
- [x] Performance Improvement
### What problem does this PR solve?
The NVIDIA Go driver in `internal/entity/models/nvidia.go` shipped with
a stub `Encode`
method that returned `no such method`. `conf/models/nvidia.json` already
lists
`nvidia/llama-3.2-nemoretriever-1b-vlm-embed-v1` as an embedding model,
but the conf had
no `embedding` URL suffix, so the picker had nothing wired even if
`Encode` worked.
A tenant who wanted to use NVIDIA NIM for chat (already working) and
embeddings from a
single provider could not, even though the upstream endpoint is public
at
`https://integrate.api.nvidia.com/v1/embeddings` and uses an
OpenAI-compatible request
body extended with the NVIDIA-specific `input_type` and `truncate`
fields. Several other
Go drivers already implement `Encode` (siliconflow, zhipu-ai, aliyun),
so the interface
and the pattern are well-established.
This PR fills the gap.
### What this PR includes
* `conf/models/nvidia.json`: declare the `embedding` URL suffix
alongside the existing
`chat` and `models` entries. The embedding model entry was already
present, so no
model addition is needed.
* `internal/entity/models/nvidia.go`: replace the `Encode` stub with a
real
implementation. Adds a small local response type that matches the
OpenAI-compatible
shape NVIDIA NIM returns.
No factory change. No interface change.
### How the driver works
* Validates `apiConfig` and the API key, validates the model name,
resolves the region
with a default fallback (matching the pattern the merged `ListModels`
and
`CheckConnection` paths in this driver already use), and builds the URL
from
`BaseURL[region] + URLSuffix.Embedding`.
* Sends all input texts in one request as the `input` array, with the
NVIDIA-specific `input_type: "query"`, `encoding_format: "float"`, and
`truncate: "END"`
fields, mirroring the Python `NvidiaEmbed` reference.
* Parses `data[*].embedding` and copies each slice into `[][]float64`
indexed by
`data[*].index` so the output order matches the input order even if the
API returns
items in a different order.
* Handles both `float64` and `float32` element types.
* Empty input returns `[][]float64{}` with no HTTP call.
* Non-200 responses propagate the upstream status line and body.
* A final pass checks every input slot got a vector and returns a clear
error if any
slot is still nil.
* Per-call 30s context deadline so a slow call cannot block forever.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
* `go build ./internal/entity/models/...` returns exit 0.
* `go vet ./internal/entity/models/...` is clean.
* `gofmt -l internal/entity/models/nvidia.go` is clean.
* The full method set on `NvidiaModel` still matches the `ModelDriver`
interface.
* Pattern parity with the just-merged Aliyun `Encode` (#14647).
Closes#14699
### What problem does this PR solve?
The Ollama Go driver shipped with a stub \`Encode\` method that returned
\`no such method\`, even though Ollama is one of the most common local
LLM runners and exposes an OpenAI-compatible embeddings endpoint at
\`/v1/embeddings\`.
Ollama users routinely run local embedding models such as
\`nomic-embed-text\`, \`mxbai-embed-large\`, or \`bge-m3\`.
Pulled with \`ollama pull <model>\` and served on the same \`/v1\`
namespace as chat. The existing \`ListModels\` already
discovers them, but because \`Encode\` was a stub, a tenant who picked
one of these models in the Go layer could not
actually run an embedding call.
### What this PR includes
- \`conf/models/ollama.json\`: add \`\"embedding\": \"embeddings\"\`
under \`url_suffix\` so the
driver can build the URL from config.
- \`internal/entity/models/ollama.go\`: replace the \`Encode\` stub with
a real implementation. Adds a small local response
type that matches the OpenAI-compatible shape.
No factory change. No interface change.
### How the driver works
- Validate the model name. The API key is optional for local Ollama, so
the Authorization header is only set when both
\`apiConfig\` and \`ApiKey\` are non-nil and non-empty, the same pattern
the recently merged CheckConnection PR (#14614) uses.
- Resolve the region with a default fallback. Return a clear "missing
base URL" error when the user has not configured
the local access address yet.
- Use a per-call \`context.WithTimeout(30s)\` and
\`http.NewRequestWithContext\`, the same pattern the merged
Aliyun Encode (#14647) uses.
- Send \`{model, input: [texts]}\` in one request.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\`, so the output
order matches the input order.
- Handle both \`float64\` and \`float32\` element types.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Length mismatch between input and result, out-of-range index, and any
missing slot all return clear errors instead
of silent zero vectors.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`OllamaModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the merged Aliyun Encode (#14647) and the existing
SiliconFlow Encode.
Closes#14662
### What problem does this PR solve?
The vLLM Go driver shipped with a stub \`Encode\` method that returned
\`not implemented\`, even though vLLM is one of the most common
production-grade self-hosted inference servers and exposes an
OpenAI-compatible embeddings endpoint at \`/v1/embeddings\`.
Users who self-host \`BAAI/bge-m3\`, \`Qwen3-Embedding-*\`,
\`NV-Embed-v2\`, or similar models on vLLM could not run an embedding
call through the Go layer. The existing \`ListModels\` already discovers
the loaded models, but the embedding path failed because \`Encode\` was
a stub.
### What this PR includes
- \`conf/models/vllm.json\`: add \`\"embedding\": \"embeddings\"\` under
\`url_suffix\` so the driver can build the URL from config.
- \`internal/entity/models/vllm.go\`: replace the \`Encode\` stub with a
real implementation. Adds a small local response
type that matches the OpenAI-compatible shape.
No factory change. No interface change.
### How the driver works
- Validate the model name. The API key is optional for self-hosted vLLM,
so the Authorization header is only set when both \`apiConfig\` and
\`ApiKey\` are non-nil and non-empty, the same pattern the recently
merged CheckConnection PR (#14614) uses.
- Resolve the region with a default fallback. Return a clear "missing
base URL" error when the user has not configured
the local access address yet.
- Use a per-call \`context.WithTimeout(30s)\` and
\`http.NewRequestWithContext\`, the same pattern the merged
Aliyun Encode (#14647) and in-flight Ollama Encode (#14664) use.
- Send \`{model, input: [texts]}\` in one request.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\`, so the output
order matches the input order.
- Handle both \`float64\` and \`float32\` element types.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Length mismatch between input and result, out-of-range index, and any
missing slot all return clear errors instead
of silent zero vectors.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`VllmModel\` still matches the \`ModelDriver\`
interface.
- Pattern parity with the merged Aliyun Encode (#14647), the in-flight
Ollama Encode (#14664), and the existing
SiliconFlow Encode.
Closes#14687
### What problem does this PR solve?
The Gitee AI Go driver in `internal/entity/models/gitee.go` shipped with
a stub `Encode` method that returned `gitee, no such method`, even
though `conf/models/gitee.json` already wires the `embedding` URL
suffix. The conf also listed no embedding models, so the picker had
nothing to select.
This blocked any tenant who wanted to use Gitee AI for chat, rerank
(already working, see #14656), and embeddings from a single provider.
This PR fills the gap, mirroring the just-merged Aliyun `Encode`
(#14647):
- `internal/entity/models/gitee.go`: replace the `Encode` stub with a
real implementation.
Validates inputs, resolves the region with a default fallback, POSTs the
standard OpenAI-compatible `{"model", "input": [...]}` body to
`BaseURL[region] + URLSuffix.Embedding`, parses `data[*].embedding`
indexed by `data[*].index` so output order matches input order, handles
both `float64` and `float32` element types, and uses a 30s per-call
context deadline matching the merged `Rerank`.
- `conf/models/gitee.json`: add `BAAI/bge-m3` so the embedding picker
has something to select.
No factory change. No interface change. No URL suffix change.
Verified with `go build`, `go vet`, and `gofmt -l` : all clean.
Closes#14697
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Two bugs in the Aliyun Go driver:
1. **`Name()` returns `"siliconflow"`** — a copy-paste bug from when the
driver was created. `Name()` is used in error messages and log output,
so every Aliyun error incorrectly attributed itself to SiliconFlow.
2. **Silent empty URL for unknown regions in `ChatWithMessages`,
`ChatStreamlyWithSender`, and `ListModels`** — all three methods
construct the request URL as `z.BaseURL[region]` without checking
whether the key exists. For an unrecognised region this returns `""`,
producing a malformed URL like `"/chat/completions"` that the HTTP
transport rejects with a confusing error. `Encode` and `Rerank` (already
merged) correctly fall back to `"default"` and return a clear error.
This PR applies the same pattern to the remaining three methods.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Closes#14703
`GoogleModel.CheckConnection` currently returns a hardcoded `no such
method` error even though the Google Go driver already supports
`ListModels`. This makes provider connection checks fail regardless of
whether the configured API key can list Google models.
This PR makes `CheckConnection` call `ListModels`, adds a small API-key
guard for nil, empty, and whitespace-only keys, and keeps `ListModels`
useful by following paginated Google model responses.
### What stays unchanged
* Google model listing still uses the Google GenAI SDK with
`genai.BackendGeminiAPI`.
* Model names still come from `models.Items[*].Name`.
* `Balance`, `Encode`, chat, streaming, provider config, and factory
wiring are unchanged.
### Tests and validation
Added focused unit coverage for:
* `CheckConnection` delegating to `ListModels` and returning its error
* nil, missing, empty, and whitespace-only API key validation
* model-name passthrough from the list-models adapter
* paginated model listing, empty-result preservation, and next-page
error propagation
Validated current PR head `17ceef43515ba8c46c254dd349b9085bf26dcbea`
locally with Go 1.25.0:
* `go test ./internal/entity/models -run
'TestGoogleModel|TestCollectGoogleModelNames' -count=1 -v` - PASS
* `go test ./internal/entity/models -count=1` - PASS
* `go test -race ./internal/entity/models -count=1` - PASS
* `gofmt -w internal/entity/models/google.go
internal/entity/models/google_test.go` - PASS, no diff
* `git diff --check` - PASS
### Type of change
* [x] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
- Implements the `Encode` method in the Google Gemini driver, which was
previously a stub returning `not implemented`
- Uses the `google.golang.org/genai` SDK's `EmbedContent` API, which
routes to the `batchEmbedContents` endpoint internally — all texts are
sent in a single request
- Adds `text-embedding-004` (max 2048 tokens) to
`conf/models/google.json`
- Response values are `[]float32` from the SDK and are cast to
`[]float64` to satisfy the `ModelDriver` interface
## Files changed
- `internal/entity/models/google.go` — full `Encode` implementation
- `conf/models/google.json` — adds `text-embedding-004` embedding model
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
The VolcEngine Go driver in `internal/entity/models/volcengine.go`
shipped with a
`ListModels` stub that returned `volcengine, no such method`.
`conf/models/volcengine.json`
also did not declare a `models` URL suffix, so the model picker had
nothing to call even
if the method body were filled in.
A tenant who configured Volcengine (Doubao / Ark) as a provider could
not see the list of
available endpoints from the RAGFlow UI. Several other Go drivers
already implement
`ListModels` against the OpenAI-compatible `/models` endpoint (deepseek,
gitee, nvidia,
openai, siliconflow), so the interface and pattern are well-established.
This PR fills the gap.
### What this PR includes
* `conf/models/volcengine.json`: declare the `models` URL suffix
alongside the existing
`chat`, `files`, and `embedding` entries. The Ark v3 API exposes
`https://ark.cn-beijing.volces.com/api/v3/models`, so the suffix is just
`models`.
* `internal/entity/models/volcengine.go`: replace the `ListModels` stub
with a real
implementation. Reuses the package-level `DSModelList` / `DSModel` types
that
DeepSeek, Gitee, and SiliconFlow already use to parse the
OpenAI-compatible models
response shape.
No factory change. No interface change.
### How the driver works
* Resolves the region with a default fallback, the same way the other
VolcEngine methods
in this driver already do.
* Builds the URL from `BaseURL[region] + URLSuffix.Models`, with
`strings.TrimSuffix` on
the base to keep the join robust.
* Issues a `GET` with optional `Authorization: Bearer <api_key>` (the
header is omitted
when no key is configured, mirroring the existing NVIDIA `ListModels`).
* Reads the response body once, surfaces a non-200 with the upstream
status line plus
body, and parses the JSON via the shared `DSModelList` type.
* Returns the model id list in input order. When the response includes
an `owned_by`
field, the entry is rendered as `id@owned_by`, matching the convention
used by the
other Go drivers.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
* `go build ./internal/entity/models/...` returns exit 0.
* `go vet ./internal/entity/models/...` is clean.
* `gofmt -l internal/entity/models/volcengine.go` is clean.
* The full method set on `VolcEngine` still matches the `ModelDriver`
interface.
* Endpoint reachability check: `GET
https://ark.cn-beijing.volces.com/api/v3/models`
returns `401 Unauthorized` without an API key, confirming the path
exists and accepts
Bearer authentication.
* Pattern parity with DeepSeek, Gitee, NVIDIA, and SiliconFlow
`ListModels`.
Fixes#14701
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The OpenAI Go driver landed in #14605 with chat, list models, and check
connection. Encode was left as a stub that returns \`not implemented\`.
\`conf/models/openai.json\` already lists three embedding models out of
the box:
- text-embedding-ada-002
- text-embedding-3-small
- text-embedding-3-large
So a tenant who picked one of these in the Go layer could not actually
run an embedding call. This PR fills the gap.
### What this PR includes
- \`conf/models/openai.json\`: add \`\"embedding\": \"embeddings\"\`
under \`url_suffix\` so the driver can build the URL from config. This
matches the \`URLSuffix.Embedding\` field used by other drivers
(siliconflow, zhipu-ai).
- \`internal/entity/models/openai.go\`: replace the Encode stub with a
real implementation that POSTs to \`/v1/embeddings\`. Adds a small local
response type \`openaiEmbeddingResponse\`.
No factory change. No interface change.
### How the implementation works
- Validate \`apiConfig\` and the API key, validate the model name. Use
the existing \`baseURLForRegion\` helper so an unknown region fails fast
with a clear error.
- Wrap the request with \`context.WithTimeout(nonStreamCallTimeout)\` so
the call has a clear deadline. Same pattern as \`ChatWithMessages\` and
\`ListModels\` already use in this file.
- Send all input texts in one request. The OpenAI API accepts the
\`input\` field as an array.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\` so the output order matches the input order
even if the API returns items in a different order.
- Handle both \`float64\` and \`float32\` element types, the way the
SiliconFlow driver does.
- An empty input slice returns \`[][]float64{}\` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks that every input slot got a vector. If any slot is
still nil, return a clear error so the caller does not silently use a
zero vector.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
(the go.mod minimum) returns exit 0.
- The full method set on \`OpenAIModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Encode implementation
(\`internal/entity/models/siliconflow.go\`).
Closes#14629
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
As title.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
top_n is missing
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR completes the Baidu Qianfan provider integration in RAGFlow.
**The following functionalities are now supported:**
- [x] Chat / Think Chat / Stream Chat / Stream Think Chat
- [x] Embedding
- [x] Rerank
- [x] Model listing
- [x] Provider connection checking
- [ ] Balance
-----
**Verified examples from the CLI:**
```plaintext
RAGFlow(user)> embed text 'what is rag' 'who are you' with 'embedding-3@test@zhipu-ai' dimension 16;
+-----------+-------+
| dimension | index |
+-----------+-------+
| 16 | 0 |
| 16 | 1 |
+-----------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'qwen3-reranker-4b@test@baidu' top 2;
+-------+---------------------+
| index | relevance_score |
+-------+---------------------+
| 0 | 0.974821150302887 |
| 1 | 0.14223189651966095 |
| 2 | 0.08632347732782364 |
+-------+---------------------+
RAGFlow(user)> think chat with 'deepseek-v3.2@test@baidu' message 'who r u'
Thinking: Hmm, the user is asking for a simple introduction. This is straightforward – no need for overcomplication.
I should give a clear, friendly response that covers my basic identity as an AI assistant, my purpose, and my capabilities. Keeping it concise but informative is key here.
Mentioning my creator Anthropic adds credibility, and ending with an offer to help invites further interaction. No need for technical details unless the user asks later.
Answer: Hello! I'm an AI assistant created by Anthropic, designed to help with a wide variety of tasks. You can think of me as a helpful digital companion—I can answer questions, assist with writing, help solve problems, provide explanations, and engage in conversation on many topics. I'm here to help with whatever you need! How can I assist you today?
Time: 8.103902
RAGFlow(user)> stream think chat with 'deepseek-v3.2@test@baidu' message 'who r u'
Thinking: mm, the user is asking "who r u" with casual spelling. This is a straightforward identity question. should give a clear, friendly introduction without overcomplicating it. Can start with my core function as an AI assistant, mention my creator, and briefly state my key capabilities. response should be welcoming and invite further interaction since this seems like an introductory question. Keeping it concise but covering the essentials: who I am, what I do, and how I can help.
Answer: ! I am DeepSeek, an AI assistant created by DeepSeek Company. I'm designed to help answer questions, provide information, assist with various tasks, and engage in conversations on a wide range of topics. I'm here to assist you with whatever you need - whether it's answering questions, helping with analysis, writing, coding, or just having a friendly chat!Is there anything specific I can help you with today? 😊
Time: 7.219703
RAGFlow(user)> list supported models from 'baidu' 'test'
+--------------------------------------+
| model_name |
+--------------------------------------+
| ernie-3.5-8k-preview |
| ernie-4.0-8k |
| ernie-4.0-turbo-8k-latest |
| ernie-4.0-turbo-8k-preview |
| ernie-4.0-8k-preview |
| ernie-speed-pro-128k |
| ernie-char-fiction-8k |
| ernie-3.5-8k |
| ernie-3.5-128k |
| ernie-lite-pro-128k |
| ernie-novel-8k |
| ernie-4.0-turbo-8k |
| ernie-4.0-turbo-128k |
| ernie-4.0-8k-latest |
| irag-1.0 |
| ........... |
| glm-5.1 |
| ernie-image-turbo |
| deepseek-v4-pro |
| deepseek-v4-flash |
| ernie-5.1 |
+--------------------------------------+
RAGFlow(user)> check instance 'test' from 'baidu'
SUCCESS
```
Additionally, this PR fixes an incorrect error message typo:
Before:
```go
fmt.Errorf("API requestssss failed with status %d: %s : %s", ...)
```
After:
```go
fmt.Errorf("API request failed with status %d: %s", ...)
```
This PR mainly improves provider compatibility, API completeness, and
runtime stability.
### Type of change
* [x] Bug Fix (non-breaking change which fixes an issue)
* [x] New Feature (non-breaking change which adds functionality)
* [x] Refactoring
### What problem does this PR solve?
```
RAGFlow(user)> embed text 'what is rag' 'who are you' with 'embedding-3@test@zhipu-ai' dimension 16;
+-----------+-------+
| dimension | index |
+-----------+-------+
| 16 | 0 |
| 16 | 1 |
+-----------+-------+
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'rerank@test@zhipu-ai' top 2;
+-------+-----------------+
| index | relevance_score |
+-------+-----------------+
| 0 | 1 |
| 2 | 0.99999976 |
+-------+-----------------+
```
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Close#14292
## Issue
File ancestry endpoints return folder metadata without validating tenant
permissions, allowing any authenticated user to query arbitrary
`file_id` values across tenant boundaries.
## Affected Endpoints
- `GET /v1/file/parent_folder?file_id={file_id}`
- `GET /v1/file/all_parent_folder?file_id={file_id}`
- `GET /api/v1/files/{id}/ancestors`
## Root Cause
These endpoints **skip the permission check** that other file operations
(Delete, Download, Move) perform.
## Expected Permission Check
All file operations should follow this 3-step validation:
- Check file.tenant_id
- Check if user_id belongs to this tenant (via user_tenant join table)
- Check KB permission type (team permission)
**Code reference:** This is implemented in `checkFileTeamPermission()`
and used by Delete/Download/Move, but **missing** from
GetParentFolder/GetAllParentFolders.
## Reproduction
```bash
# User B (tenant: BBB) accessing User A's file (tenant: AAA)
curl -H "Authorization: Bearer USER_B_TOKEN" \
"http://localhost:9384/v1/file/parent_folder?file_id=AAA_FILE_123"
# Result: Returns User A's folder metadata ❌
# Expected: "No authorization." ✅
Fix
Pass userID from handler to service and call checkFileTeamPermission() — same as Download/Delete/Move handlers.
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
### What problem does this PR solve?
Implement `HuggingFace` provider
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
As title
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The Aliyun Go driver has a stub `Rerank` method that always returns
`"Aliyun, Rerank not implemented"`. DashScope exposes an
OpenAI-compatible rerank endpoint (`compatible-mode/v1/rerank`) and
hosts dedicated bilingual rerankers (`gte-rerank-v2`, `gte-rerank`) that
are a natural pairing with the embedding models already in
`aliyun.json`. Without this, Aliyun users cannot use reranking within
RAGFlow.
Closes#14675
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
```
RAGFlow(user)> logout;
SUCCESS
```
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
1. **Fix Global State Pollution in Local Providers (Critical Bug):** -
Resolved a severe concurrency and architecture issue in
`model_service.go`. Previously, `ListSupportedModels` would permanently
overwrite the global provider singleton with a localized URL instance
(`driver.NewInstance`). This caused cross-request contamination in
multi-tenant environments.
- Fixed `CheckProviderConnection` for local models (LM Studio, vLLM,
Ollama). It now properly creates a localized driver copy and injects the
`base_url` before testing the connection, entirely eliminating the
false-positive `missing base URL` error without polluting the global
state.
2. **Implement `VolcEngine` Embeddings:** - Fully implemented the
`Encode` method for the `volcengine` provider, enabling text embedding
capabilities for VolcEngine models.
3. **Enhance Region Validation in `SiliconFlow`:** - Added a strict
empty string check (`*apiConfig.Region != ""`) alongside the existing
`nil` check when parsing regions. This ensures that if an empty string
is passed, the system safely falls back to the `"default"` region,
preventing malformed URL requests and `unsupported protocol scheme`
errors.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
1. Update API URL
2. Add password encryption
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
The Aliyun Go driver shipped with a stub \`Encode\` method that returned
\`no such method\`, even though \`conf/models/aliyun.json\` already
wires the OpenAI-compatible embeddings URL suffix at
\`compatible-mode/v1/embeddings\`. The same config also did not list any
embedding models, so the picker had nothing to select.
So an Aliyun tenant who wanted to use Tongyi text-embedding-v3 or v4 in
the Go layer could not, even though the upstream endpoint is public and
uses the standard \`POST /v1/embeddings\` shape that the SiliconFlow and
ZhipuAI
drivers already support.
This PR fills the gap.
### What this PR includes
- \`conf/models/aliyun.json\`: add \`text-embedding-v4\` and
\`text-embedding-v3\` to the \`models\` array.
- \`internal/entity/models/aliyun.go\`: replace the \`Encode\` stub with
a real implementation. Adds a small local response type that matches the
OpenAI-compatible shape.
No factory change. No interface change.
### How the driver works
- Validate \`apiConfig\` and the API key, validate the model name,
resolve the region with a default fallback, build the
URL from \`BaseURL[region] + URLSuffix.Embedding\`.
- Send all input texts in one request as the \`input\` array, the same
OpenAI-compatible shape the SiliconFlow \`Encode\`
uses.
- Parse \`data[*].embedding\` and copy each slice into a \`[][]float64\`
indexed by \`data[*].index\` so the output order matches the input order
even if the API returns items in a different order.
- Handle both \`float64\` and \`float32\` element types.
- Empty input returns \`[][]float64{}\` with no HTTP call.
- Non-200 responses propagate the upstream status line and body.
- A final pass checks every input slot got a vector and returns a clear
error if any slot is still nil.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### How was this tested?
- \`go build ./internal/entity/models/...\` in a clean go 1.25 image
returns exit 0.
- The full method set on \`AliyunModel\` still matches the
\`ModelDriver\` interface.
- Pattern parity with the existing SiliconFlow Encode implementation.
Closes#14646
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
1. implement `rerank`, `embedding`, `balance`, `checkConnet` method for
`OpenRouter`
2. delete `chat` method in `internal/entity/models/volcengine.go`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
### What problem does this PR solve?
Since API is updated, CLI login failed.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
